Sabrina Richter

The paper integrates single-cell proteomics and transcriptomics to study human hematopoietic stem and progenitor cells, identifying protein markers and dynamics not captured by mRNA alone.

This paper introduces a multiplex-DIA workflow that enhances single-cell proteome depth using dimethyl labeling and a reference channel, achieving reliable quantification of nearly 4,000 proteins per cell.

This paper evaluates graph neural networks' ability to predict tissue-level phenotypes from spatial molecular data, finding that some phenotypes can be captured without spatial information and that overfitting limits representation learning.

The paper develops a high-sensitivity mass spectrometry workflow to quantify single-cell proteome changes upon perturbation, revealing a stable core proteome despite transcriptomic variability.

Squidpy extends Scanpy/AnnData with spatial neighbor graphs and a lazy-loading image container, enabling spatial statistics, ligand-receptor tests, image features, and ~10-fold faster neighborhood enrichment than Giotto.